1. Field of the Invention
The present invention relates to a concerned information recommendation system and method considering user's watching or listening time and the maximum playing time of contents.
2. Description of the Related Art
In an information system, personalization means provision of information or contents suitable for an individual user based on information that the user provides to the system. Information that a user can provide to the system may be generally classified into two kinds of information. First is individual user's personal information, such as concerned fields, age, sex, etc., which an individual user can directly input into the system. An example of personalization using sex, one of the personal information, is to recommend currently popular women's wear to a female user when she accesses an electronic commercial transaction system for clothing sale and to recommend currently popular men's wear to a male user when he accesses the electronic commercial transaction system.
Second is information that can be obtained by data-mining the behaviors of an individual user in the electronic commercial transaction system, e.g., an article purchasing pattern of the user in the electronic commercial transaction system and characteristics commonly included in information requested by the user. As an exemplary example, on the assumption that a result that a user having purchased article A has frequently purchased article B and article C simultaneously with the purchasing of article A is obtained through data mining, it is possible to recommend article B and article C to a customer having an intention to purchase article A in the future, thereby achieving the increase in sales.
In an information provision system having no article transaction between a user and the system, however, it is necessary to modify the electronic transaction system and method that are capable of understanding a relationship among article A, article B, and article C, described above as an example.
When a user having requested information A in the past frequently has requested information B and information C simultaneously with the request of information A, it is possible to recommend information B and information C to a user requesting information A in the future. However, when it is not confirmed how much the recommendation has been helpful to the user, it is not possible to guarantee the user's satisfaction degree of the recommended information. For example, news information may be possibly requested by a user due to its sensational title. In this case, however, there is no doubt that the user will request another piece of information without full confirmation of the requested information.
A conventional association rule inquiry method involves a possibility of deriving a wrong association rule for a reason that the concern degree of a user cannot be considered. Even an association rule discovered by an association rule inquiry method known to have a high reliability may not actually be confirmed to be information that the user has been concerned about.